code: def create_block(block, parameters, blob_writer):
      def feeds_to_only_constexprs(op):
          return (
              (op.op_type == "const")
              and len(op.outputs[0].child_ops) > 0
              and all(
                  (child_op.op_type.startswith("constexpr_"))
                  for child_op in op.outputs[0].child_ops
              )
          )
  
      proto_ops = []
  
      # Find the const op that generates classify's "label" / "class" string vec.
      classify_const_classes_op = None
      if len(block.operations) > 0:
          # Classify is always the last operation in the block.
          op = block.operations[-1]
          op_cls_name = type(op).__name__
          if op_cls_name == "classify":
              classes_var = op.inputs["classes"]
              classify_const_classes_op = classes_var.op
              if len(classes_var.child_ops) != 1:
                  raise ValueError(
                      "Classify's labels/classes should be input to only 1 op (classify)."
                  )
  
      for op in block.operations:
          op_cls_name = type(op).__name__
          if op_cls_name == "const":
              if feeds_to_only_constexprs(op):
                  continue
              # Do not serialize the const op that creates the var bound to the classifier's "classes" param.
              # The variable's value will be bound directly to classify's "classes" param instead.
              if op != classify_const_classes_op:
                  proto_ops.append(translate_const(op, blob_writer))
          elif op_cls_name.startswith("constexpr_"):
              proto_ops.append(translate_constexpr(op, blob_writer))
          elif op_cls_name == "classify":
              # Classify's "classes" param should be serialized as a value literal bound
              # directly to the param, rather than as a const-generated variable.
              proto_ops.append(
                  translate_generic_op(
                      op, parameters, blob_writer, ["classes"]
                  )
              )
          elif op_cls_name == "reshape_like":
              # The reshape_like should also be able to take value from a const op
              # This is a workaround solution
              # rdar://98689808 (Reshape_like should also accept const value from non literal input)
              literal_params = ["begins", "ends", "end_masks"]
              proto_ops.append(
                  translate_generic_op(
                      op, parameters, blob_writer, literal_params
                  )
              )
          else:
              proto_ops.append(
                  translate_generic_op(op, parameters, blob_writer)
              )
  
      inputs = []
      if not isinstance(block, Function):
          # Function is subclass of Block, but function's block has no input,
          # and hence skipping reading the block inputs.
          for var in block.inputs:
              proto_type = types_to_proto(var.sym_type)
              inputs.append(
                  pm.NamedValueType(name=var.name, type=proto_type)
              )
      output_names = [v.name for v in block.outputs]
      return pm.Block(
          inputs=inputs, outputs=output_names, operations=proto_ops
      )
  
imports: import os
  import warnings
  from typing import Optional
  import numpy
  from coremltools import _OPSET, _SPECIFICATION_VERSION_IOS_15
  from coremltools import _logger
  from coremltools.converters.mil.backend.backend_helper import _get_probability_var_for_classifier
  from coremltools.converters.mil.backend.mil.helper import cast_to_framework_io_dtype, create_file_value, create_immediate_value, create_list_scalarvalue, create_scalar_value, types_to_proto
  from coremltools.converters.mil.backend.nn.load import _set_optional_inputs
  from coremltools.converters.mil.input_types import EnumeratedShapes, ImageType, RangeDim, TensorType
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program, mil_list, types
  from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, any_variadic, is_symbolic
  from coremltools.models.neural_network.flexible_shape_utils import NeuralNetworkImageSize, NeuralNetworkImageSizeRange, add_enumerated_image_sizes, add_multiarray_ndshape_enumeration, set_multiarray_ndshape_range, update_image_size_range
  from coremltools.models.utils import _WEIGHTS_FILE_NAME
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.proto import MIL_pb2
  from coremltools.proto import Model_pb2
  from ..backend_helper import _get_colorspace_enum, _validate_image_input_output_shapes
label_desc: Use image node_id 5 for calling a global function with example usage: create_block(block, parameters, blob_writer) and returns: pm, unknown